Official Development Assistance and Institutional Quality on Undeveloped countries

Secciones

Referencias

International aid may take the form of multilateral aid – provided through international bodies such as the UN, or NGOs such as Oxfam – or bilateral aid, which operates on a government-to-government basis. There is considerable debate about whether international aid works, in the sense of reducing poverty and stimulating development.

However, the effectiveness of aid is often diluted by corruption. Aid is invariably channeled through the governments of recipient countries, in which power is often concentrated in the hands of a few politicians and bureaucrats, and the mechanisms of accountability are, at best, poorly developed. This tends to benefit corrupt leaders and elites rather than the people, projects and programs for which it was intended.

Watts, Carl. (2014). Re: Does foreign aid help the developing countries towards development?. Retrieved from: https://www.researchgate.net/post/Does_foreign_aid_help_the_developing_countries_towards_development/5322005ed039b1e7648b459c/citation/download.

The hypothesis that foreign aid can promote growth in developing countries was explored, using panel data series for foreign aid, while accounting for regional differences in Asian, African, Latin American, and the Caribbean countries as well as the differences in income levels, the results of this study also indicate that foreign aid has mixed effects on economic growth in developing countries.

Ekanayake, E. & Chatrna, Dasha. (2010). The effect of foreign aid on economic growth in developing countries. Journal of International Business and Cultural Studies. 3.

This study examines the relationships between foreign aid, institutional structure, and economic performance for 80 countries in Europe, America, Africa, and Asia. It is found that official development assistance and the quality of institutional structure in the sample countries affect economic growth positively.

Hayaloğlu, Pınar. (2023). Foreign Aid, Institutions, and Economic Performance in Developing Countries. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi. 18. 748-765. 10.17153/oguiibf.1277348.

Manual para replicar

Cargando Librerias

Algunas librerias y paquetes usados para obtener y descargar los datos

library(tidyverse) # manejo de dataframes
library(reshape2)  # para tranfromar data de long a wide
library(WDI)       # libreria para acceder a metadata de banco mundial
library(readxl)    # leer archivos de excel
library(readr)     # leer archivos csv
library(visdat)    # visualizacion de datos como graficos
library(plotly)    # graficos
library(purrr)     # funcion map
library(plm)       # modelos lineales para datos panel
library(car)       # test y utilidades para modelos
library(htmltools) # para imprimir graficos en html

Obtener datos

Datos para paises bajos ingresos sean utilizados, segun clasificación del banco mundial, hay 26 paises de bajos ingresos y 51 de ingresos medios bajos

country_class <- read_excel("CLASS.xlsx")

country_class %>%
  filter(!is.na(Region), !is.na(`Income group`)) %>%
  group_by(`Income group`) %>%
  summarise(countries = n()) %>%
  arrange(factor(`Income group`, levels = c('High income', 'Upper middle income', 'Lower middle income', 'Low income')))

Listado de paises a analisar:

my_countries <- country_class %>%
  filter(!is.na(Region), `Income group` %in% c('Low income', 'Lower middle income')) %>%
  select(Code)
my_countries %>% merge(country_class) %>% select(Code, Economy)

Hacer la respectiva asociacion de nombres iso3c e iso2c

my_countries$iso2c <- WDI_data$country %>%
  filter(iso3c %in% my_countries$Code) %>%
  .$iso2c
my_countries

Datos del banco mundial (para ODA y los indices de gobernanza) y el Human Development Reports API son descargados desde scripts de Python. Son almacenados en archivos CSV y luego son cargados aqui:

cargar HDI

datos_HDI <- read_csv("datos_python_HDI.csv", col_names = c('Code', 'iso2c', 'indicator', 'year', 'value'), 
                      col_types = list(col_character(), col_character(), col_character(), col_double(), col_double()))

hdi_indicators <- datos_HDI %>% distinct(indicator) %>% .$indicator

cargar ODA, GDP, POP.GROW

oda_indicators <- c(
'DT_ODA_ALLD_CD',
'DT_ODA_ALLD_KD',
'DT_ODA_OATL_CD',
'DT_ODA_OATL_KD',
'DT_ODA_ODAT_CD',
'DT_ODA_ODAT_GI_ZS',
'DT_ODA_ODAT_GN_ZS',
'DT_ODA_ODAT_KD',
'DT_ODA_ODAT_MP_ZS',
'DT_ODA_ODAT_PC_ZS',
'DT_ODA_ODAT_XP_ZS'
)
gob_indicators <- c(
'CC_EST',
'CC_NO_SRC',
'CC_PER_RNK',
'CC_PER_RNK_LOWER',
'CC_PER_RNK_UPPER',
'CC_STD_ERR',
'GE_EST',
'GE_NO_SRC',
'GE_PER_RNK',
'GE_PER_RNK_LOWER',
'GE_PER_RNK_UPPER',
'GE_STD_ERR',
'PV_EST',
'PV_NO_SRC',
'PV_PER_RNK',
'PV_PER_RNK_LOWER',
'PV_PER_RNK_UPPER',
'PV_STD_ERR',
'RQ_EST',
'RQ_NO_SRC',
'RQ_PER_RNK',
'RQ_PER_RNK_LOWER',
'RQ_PER_RNK_UPPER',
'RQ_STD_ERR',
'RL_EST',
'RL_NO_SRC',
'RL_PER_RNK',
'RL_PER_RNK_LOWER',
'RL_PER_RNK_UPPER',
'RL_STD_ERR',
'VA_EST',
'VA_NO_SRC',
'VA_PER_RNK',
'VA_PER_RNK_LOWER',
'VA_PER_RNK_UPPER',
'VA_STD_ERR'
)
gdp_indicators <- c(
'NY_ADJ_NNTY_PC_CD',
'NY_ADJ_NNTY_PC_KD',
'NY_ADJ_NNTY_PC_KD_ZG',
'NY_GDP_PCAP_CN',
'NY_GDP_PCAP_KN',
'NY_GDP_PCAP_CD',
'NY_GDP_PCAP_KD',
'NY_GDP_MKTP_KD_ZG',
'NY_GDP_DEFL_ZS_AD',
'NY_GDP_DEFL_ZS',
'NY_GDP_MKTP_CD',
'NY_GDP_MKTP_CN',
'NY_GDP_MKTP_KN',
'NY_GDP_MKTP_KD',
'NY_GDP_PCAP_KD_ZG',
'NY_GDP_PCAP_PP_KD',
'NY_GDP_PCAP_PP_CD',
'SL_GDP_PCAP_EM_KD',
'SP_POP_GROW'
)

datos_WB <- data.frame(indicator = character(), iso2c = character(), year = double(), value = double())

suppressWarnings(
  for (indicator in c(oda_indicators, gob_indicators, gdp_indicators)) {
    datos_WB <- rbind(datos_WB, read_csv(paste("datos_python", indicator, ".csv", sep =''), 
                                           col_names = c('indicator', 'iso2c', 'year', 'value'),
                                           col_types = list(col_character(), col_character(), col_double(), col_double())))
  }
)

cargar POVERTY

Poverty <- read_excel("GlobalExtremePovertyDollaraDay_Compact.xlsx", sheet = "Data Long Format")

names(Poverty) <- c("ccode", "country", "year", "value")

Poverty[Poverty=="Cape Verde"] <- "Cabo Verde"
Poverty[Poverty=="Congo"] <- "Congo, Rep."
Poverty[Poverty=="Egypt"] <- "Egypt, Arab Rep."
Poverty[Poverty=="Iran"] <- "Iran, Islamic Rep."
Poverty[Poverty=="Kyrgyzstan"] <- "Kyrgyz Republic"
Poverty[Poverty=="Laos"] <- "Lao PDR"
Poverty[Poverty=="Macedonia"] <- "North Macedonia"
Poverty[Poverty=="Russia"] <- "Russian Federation"
Poverty[Poverty=="Slovakia"] <- "Slovak Republic"
Poverty[Poverty=="South Korea"] <- "Korea, Rep."
Poverty[Poverty=="Swaziland"] <- "Eswatini"
Poverty[Poverty=="Syria"] <- "Syrian Arab Republic"
Poverty[Poverty=="The Gambia"] <- "Gambia, The"
Poverty[Poverty=="Turkey"] <- "Turkiye"
Poverty[Poverty=="Venezuela"] <- "Venezuela, RB"
Poverty[Poverty=="Yemen"] <- "Yemen, Rep."

Poverty <- Poverty %>%
  filter(year > 1994) %>%
  merge(WDI_data$country, all.x = TRUE) %>%
  mutate(indicator = 'POV') %>%
  merge(my_countries) %>%
  select(indicator, iso2c, year, value)

cargar Political Civil Liberties

Manipulacion de Datos

Transformar datos a la estructura wide

datos_paper <- rbind(datos_WB, datos_HDI %>% select(indicator, iso2c, year, value), Poverty, PC_LIB) %>%
  pivot_wider(names_from = indicator, values_from = value)

Promedio de Indices de Gobernanza

datos_paper <- datos_paper %>% mutate(GOV =  (CC.EST + GE.EST + PV.EST + RQ.EST + RL.EST + VA.EST) / 6)

Operador Diferencia

datos_paper <- datos_paper %>% arrange(iso2c, year) %>% 
        mutate(hdi_diff = case_when(iso2c == dplyr::lag(iso2c) ~ hdi - dplyr::lag(hdi), TRUE ~ NA_real_), 
               NY.GDP.PCAP.CD_diff = case_when(iso2c == dplyr::lag(iso2c) ~ NY.GDP.PCAP.CD - dplyr::lag(NY.GDP.PCAP.CD), TRUE ~ NA_real_),
               DT.ODA.ALLD.CD_diff = case_when(iso2c == dplyr::lag(iso2c) ~ DT.ODA.ALLD.CD - dplyr::lag(DT.ODA.ALLD.CD), TRUE ~ NA_real_),
               DT.ODA.ODAT.PC.ZS_diff = case_when(iso2c == dplyr::lag(iso2c) ~ DT.ODA.ODAT.PC.ZS - dplyr::lag(DT.ODA.ODAT.PC.ZS), TRUE ~ NA_real_),
               GOV_diff = case_when(iso2c == dplyr::lag(iso2c) ~ GOV - dplyr::lag(GOV), TRUE ~ NA_real_),
               POV_diff = case_when(iso2c == dplyr::lag(iso2c) ~ POV - dplyr::lag(POV), TRUE ~ NA_real_))

Clasificaciones dicotomicas

Visualizacion de Datos

ODA

vis_dat(datos_paper %>% select(all_of(gsub("_", ".", oda_indicators)))) 

  # DT.ODA.OATL.CD and DT.ODA.OATL.KD faltan
  # DT.ODA.ODAT.GI.ZS, DT.ODA.ODAT.GN.ZS, DT.ODA.ODAT.MP.ZS and DT.ODA.ODAT.XP.ZS tienen faltas
  # Un par de ocurrencias pais-año que faltan datos

GDP

vis_dat(datos_paper %>% select(NY.GDP.PCAP.CN, NY.GDP.PCAP.CD)) 

  # NY.GDP.PCAP.CN, NY.GDP.PCAP.CD, NY.GDP.MKTP.CD, NY.GDP.MKTP.CN son buenos candidatos para usar como variables, 
  # 'SY'falta PIB per Capita en 2022, 2023 sin datos algunos paises

GOV

vis_dat(datos_paper %>% arrange(year) %>% select(all_of(gsub("_", ".", gob_indicators)))) 

  # Datos del 2000 para atras tienen espacios faltantes 

HDI

vis_dat(datos_paper %>% select(all_of(hdi_indicators))) 

  # abr, co2_prod, le, le_f, le_m, mmr son las pocas categorias sin datos faltantes
  # hdi faltante en multiples ocaciones

POP.GROW

vis_dat(datos_paper %>% arrange(iso2c) %>% select(SP.POP.GROW)) 

  # ZW no tiene datos de crecimiento poblacional

POV

vis_dat(datos_paper %>% arrange(iso2c) %>% select(POV)) 

# Hay muchos paises sin datos

POLITICAL CIVIL LIBERTY

Modelos

Filtros para modelo

# variables de etiqueta
ve <- c('iso2c', 'year')
# variables depndientes
vd <- c('hdi')                # 'hdi_diff', 'NY.GDP.PCAP.CD', 'NY.GDP.PCAP.CD_diff'
# variables independientes
vi <- c('DT.ODA.ODAT.PC.ZS')  # 'DT.ODA.ALLD.CD', 'DT.ODA.ALLD.CD_diff', 'DT.ODA.ODAT.PC.ZS_diff'
# variables de control
vc <- c('NY.GDP.PCAP.CD', 'SP.POP.GROW', 'GOV') # 'CC.EST', 'GE.EST', 'PV.EST', 'RQ.EST', 'RL.EST', 'VA.EST', 'GOV_diff', 'POV', 'POV_diff', 'POL.CIV.LIB'
# variables interactivas
vint <- c() # 'GOV_GOOD'

# paises sin datos
delete_c <- c('SS', 'ZW', 'BT', 'ER', 'GW', 'KP', 'LB', 'NG', 'PS', 'SO', 'VU', 'FM', 'KI', 'TL', 'CV', 'SB', 'SY')
          #, 'KI',  'MR',   'SD',   'WS' Si se usa POL.CIV.LIB
# años sin datos
first_y <- 2002
last_y <- 2022

datos_model <- datos_paper %>% 
  filter(!iso2c %in% delete_c, !year <  first_y, !year > last_y) %>%
  select(all_of(c(ve, vd, vi, vc, vint)))

f <- paste(vd, '~', case_when(length(vint) > 0 ~ paste(vi, vint, sep = '*'), TRUE ~ vi), '+', paste(vc, collapse = ' + '))

datos_model

vis_dat(datos_model)

Relaciones

Se revisara las relaciones entre las variables graficamente

my_plot = list()

for (vd_ in vd) {
  for (vi_ in c(vi, vc)){
    fit <- lm(paste(vd_, '~', vi_) ,data = datos_model)
    my_plot[[paste(vd_,vi_)]] <- plot_ly(x = datos_model[[vi_]], 
                                         y = datos_model[[vd_]], 
                                         type = 'scatter', 
                                         mode = 'markers', 
                                         name = vi_) %>%
      add_lines(x = datos_model[[vi_]], fitted(fit), name = paste("trace", vi_))
  }
}

subplot(my_plot, nrows = 2, margin = 0.05)  %>% layout(title = vd)

NA

Modelo OLS

print(f)

[1] "hdi ~ DT.ODA.ODAT.PC.ZS + NY.GDP.PCAP.CD + SP.POP.GROW + GOV"

model_ols <- lm(f, data=datos_model)
summary(model_ols)

Call:
lm(formula = f, data = datos_model)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.247675 -0.036913  0.002571  0.037526  0.286912 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        5.286e-01  7.700e-03  68.654  < 2e-16 ***
DT.ODA.ODAT.PC.ZS -6.839e-05  2.791e-05  -2.450   0.0144 *  
NY.GDP.PCAP.CD     6.106e-05  2.112e-06  28.916  < 2e-16 ***
SP.POP.GROW       -2.672e-02  2.065e-03 -12.936  < 2e-16 ***
GOV                2.629e-02  4.685e-03   5.611 2.47e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.06869 on 1255 degrees of freedom
Multiple R-squared:  0.5791,    Adjusted R-squared:  0.5778 
F-statistic: 431.8 on 4 and 1255 DF,  p-value: < 2.2e-16

residualPlots(model_ols)

                  Test stat Pr(>|Test stat|)    
DT.ODA.ODAT.PC.ZS    1.4508           0.1471    
NY.GDP.PCAP.CD      -9.6904        < 2.2e-16 ***
SP.POP.GROW          5.1476         3.06e-07 ***
GOV                 -0.4570           0.6477    
Tukey test          -9.0780        < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

plot(model_ols)

Modelo Fixed Effects

print(f)

[1] "hdi ~ DT.ODA.ODAT.PC.ZS + NY.GDP.PCAP.CD + SP.POP.GROW + GOV"

model_fe <- plm(f, data=datos_model, index = ve, model = "within")
summary(model_fe)

Oneway (individual) effect Within Model

Call:
plm(formula = f, data = datos_model, model = "within", index = ve)

Balanced Panel: n = 60, T = 21, N = 1260

Residuals:
       Min.     1st Qu.      Median     3rd Qu.        Max. 
-0.09232032 -0.01797056  0.00058926  0.01843193  0.12071813 

Coefficients:
                     Estimate  Std. Error t-value  Pr(>|t|)    
DT.ODA.ODAT.PC.ZS  2.0738e-05  2.1585e-05  0.9608  0.336868    
NY.GDP.PCAP.CD     4.3412e-05  1.5485e-06 28.0354 < 2.2e-16 ***
SP.POP.GROW       -4.8699e-03  1.5577e-03 -3.1263  0.001813 ** 
GOV                2.2056e-02  5.2023e-03  4.2396 2.412e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Total Sum of Squares:    1.7772
Residual Sum of Squares: 0.99317
R-Squared:      0.44116
Adj. R-Squared: 0.41172
F-statistic: 236.033 on 4 and 1196 DF, p-value: < 2.22e-16

#summary(lm(paste(f, '+ iso2c'), data=datos_model))

Modelo Random Effects

print(f)

[1] "hdi ~ DT.ODA.ODAT.PC.ZS + NY.GDP.PCAP.CD + SP.POP.GROW + GOV"

model_re <- plm(f, data=datos_model, index = ve, model = "random")
summary(model_re)

Oneway (individual) effect Random Effect Model 
   (Swamy-Arora's transformation)

Call:
plm(formula = f, data = datos_model, model = "random", index = ve)

Balanced Panel: n = 60, T = 21, N = 1260

Effects:
                    var   std.dev share
idiosyncratic 0.0008304 0.0288169 0.176
individual    0.0038800 0.0622894 0.824
theta: 0.8996

Residuals:
      Min.    1st Qu.     Median    3rd Qu.       Max. 
-0.0981167 -0.0169489  0.0024302  0.0188212  0.1164123 

Coefficients:
                     Estimate  Std. Error z-value  Pr(>|z|)    
(Intercept)        4.9722e-01  1.0052e-02 49.4631 < 2.2e-16 ***
DT.ODA.ODAT.PC.ZS  1.8031e-05  2.1445e-05  0.8408  0.400441    
NY.GDP.PCAP.CD     4.4153e-05  1.5413e-06 28.6459 < 2.2e-16 ***
SP.POP.GROW       -5.7962e-03  1.5481e-03 -3.7441  0.000181 ***
GOV                2.3674e-02  5.0541e-03  4.6841 2.812e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Total Sum of Squares:    1.9012
Residual Sum of Squares: 1.0559
R-Squared:      0.44464
Adj. R-Squared: 0.44287
Chisq: 1004.8 on 4 DF, p-value: < 2.22e-16

Hausman Test

print(f)

[1] "hdi ~ DT.ODA.ODAT.PC.ZS + NY.GDP.PCAP.CD + SP.POP.GROW + GOV"

phtest(model_fe, model_re)

    Hausman Test

data:  f
chisq = 35.273, df = 4, p-value = 4.082e-07
alternative hypothesis: one model is inconsistent